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Learning-To-Rank

Learning to rank is the application of machine learning to build ranking models. Some common use cases for ranking models are information retrieval (e.g., web search) and news feeds application (think Twitter, Facebook, Instagram).

Papers

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Showing 731–740 of 753 papers

Title	Date	Tasks	Status	Hype
Learning to Extract Folktale Keywords	Aug 1, 2013	Keyword ExtractionLearning-To-Rank	—Unverified	0
Learning to Order Natural Language Texts	Aug 1, 2013	Concept-To-Text GenerationDocument Summarization	—Unverified	0
Resolving Entity Morphs in Censored Data	Aug 1, 2013	Learning-To-Rank	—Unverified	0
Reranking with Linguistic and Semantic Features for Arabic Optical Character Recognition	Aug 1, 2013	Language ModellingLearning-To-Rank	—Unverified	0
Introducing LETOR 4.0 Datasets	Jun 9, 2013	Learning-To-Rank	CodeCode Available	1
Learning to Rank for Expert Search in Digital Libraries of Academic Publications	Feb 2, 2013	Information RetrievalLearning-To-Rank	—Unverified	0
Visualization on Financial Terms via Risk Ranking from Financial Reports	Dec 1, 2012	Learning-To-Rank	—Unverified	0
Expected Divergence Based Feature Selection for Learning to Rank	Dec 1, 2012	feature selectionLearning-To-Rank	—Unverified	0
RelationListwise for Query-Focused Multi-Document Summarization	Dec 1, 2012	Document SummarizationLearning-To-Rank	—Unverified	0
Extraction of Domain-Specific Bilingual Lexicon from Comparable Corpora: Compositional Translation and Ranking	Dec 1, 2012	Learning-To-RankMachine Translation	—Unverified	0

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